Methods and systems for selecting an alimentary transfer descriptor using categorical constraints

ABSTRACT

A system for selecting an alimentary transfer descriptor includes a process selection device configured to receive an alimentary transfer request including at least a description of an alimentary collation and at least a terminal location, provide at least categorical constraint modifying the alimentary transfer request, and transmit a selected alimentary transfer descriptor to a physical performance entity, a descriptor generator module configured to generate a plurality of alimentary transfer descriptors each describing a physical transfer process to be performed by a physical performance entity, an alimentary collation to be provided during a corresponding physical transfer process, and a plurality of attributes, each attribute corresponding to a selection criterion of a plurality of selection criteria, and a selector module designed and configured to select an alimentary transfer descriptor of the plurality of alimentary transfer descriptors by executing a selection function on the plurality of alimentary transfer descriptors.

FIELD OF THE INVENTION

The present invention generally relates to the field of networkcommunication. In particular, the present invention is directed toselecting an alimentary transfer descriptor using categoricalconstraints.

BACKGROUND

Efficient routing of instructions for physical performance of actionsvia network communication remains an elusive goal. Particularly wheresuch actions include transfer of items of an alimentary nature, wheremultiple issues of timing, speed, quality, and necessity affect thechoice in question, selection and communication processes often fallshort of ideal solutions.

SUMMARY OF THE DISCLOSURE

In one aspect, a method of selecting an alimentary transfer descriptorusing categorical constraints includes receiving, by a process selectiondevice an alimentary transfer request including at least a descriptionof an alimentary collation and at least a terminal location. The methodincludes providing, by the process selection device, at leastcategorical constraint modifying the alimentary transfer request. Themethod includes generating, by the process selection device, a pluralityof alimentary transfer descriptors, where each alimentary transferdescriptor describes a physical transfer process, of a plurality ofphysical transfer processes, to be performed by a corresponding physicalperformance entity of a plurality of physical performance entities, eachalimentary transfer descriptor describes an alimentary collation to beprovided during a corresponding physical transfer process, and eachalimentary transfer descriptor further includes a plurality ofattributes, each attribute corresponding to a selection criterion of aplurality of selection criteria. The method includes selecting, by theprocess selection device, an alimentary transfer descriptor of theplurality of alimentary transfer descriptors, wherein selecting includesexecuting a selection function on the plurality of alimentary transferdescriptors, the selection function generating a selection output as afunction of the plurality of selection criteria, the plurality ofattributes, and the categorical constraint, and selecting the alimentarytransfer descriptor based on the selection output. The method includestransmitting, by the process selection device, the selected alimentarytransfer descriptor to the physical performance entity corresponding tothe selected alimentary transfer descriptor.

In another aspect, a system for selecting an alimentary transferdescriptor using categorical constraints includes a process selectiondevice, the process selection device designed and configured to receivean alimentary transfer request including at least a description of analimentary collation and at least a terminal location, and provide atleast categorical constraint modifying the alimentary transfer request,and transmit a selected alimentary transfer descriptor to the physicalperformance entity corresponding to the selected alimentary transferdescriptor. The system includes a descriptor generator module operatingon the process selection device, the descriptor generator moduledesigned and configured to generate a plurality of alimentary transferdescriptors, wherein each alimentary transfer descriptor describes aphysical transfer process, of a plurality of physical transferprocesses, to be performed by a corresponding physical performanceentity of a plurality of physical performance entities, each alimentarytransfer descriptor describes an alimentary collation to be providedduring a corresponding physical transfer process, and each alimentarytransfer descriptor further includes a plurality of attributes, eachattribute corresponding to a selection criterion of a plurality ofselection criteria. The system includes a selector module operating onthe process selection device, the selector module designed andconfigured to select an alimentary transfer descriptor of the pluralityof alimentary transfer descriptors. Selecting includes executing aselection function on the plurality of alimentary transfer descriptors,wherein the selection function generates a selection output as afunction of the plurality of selection criteria, the plurality ofattributes, and the categorical constraint selecting the alimentarytransfer descriptor based on the selection output.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for selecting an alimentary transfer descriptor using categoricalconstraints;

FIG. 2 is a block diagram illustrating an exemplary embodiment of acollation element database;

FIG. 3 is a block diagram illustrating an exemplary embodiment of a userhistory database;

FIG. 4 is a block diagram illustrating an exemplary embodiment of a userconstraint database;

FIG. 5 is a block diagram illustrating an exemplary embodiment of adietary data database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of aselection module;

FIG. 7 is a flow diagram illustrating an exemplary embodiment of amethod of selecting an alimentary transfer descriptor using categoricalconstraints; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Embodiments of disclosed systems and methods select processes fortransfer of alimentary collations, and entities to effect physicalperformance thereof, by reference to a selection function accounting formultiple attributes of such processes and/or entities. Loss functionanalysis may balance factors; this may be done by reference to aniteratively generated expression of preferential allocations of suchattributes, for instance by using machine-learning processes to acquireaccurate pictures of user preferences.

Referring now FIG. 1, an exemplary embodiment of a system 100 forselecting an alimentary transfer descriptor using categoricalconstraints is illustrated. System 100 includes a process selectiondevice 104. Process selection device 104 may include any computingdevice as described below in reference to FIG. 10, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described below in reference toFIG. 10. Process selection device 104 may be housed with, may beincorporated in, or may incorporate one or more sensors of at least asensor. Computing device may include, be included in, and/or communicatewith a mobile device such as a mobile telephone or smartphone. Processselection device 104 may include a single computing device operatingindependently, or may include two or more computing device operating inconcert, in parallel, sequentially or the like; two or more computingdevices may be included together in a single computing device or in twoor more computing devices. Process selection device 104 with one or moreadditional devices as described below in further detail via a networkinterface device. Network interface device may be utilized forconnecting a process selection device 104 to one or more of a variety ofnetworks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. Process selection device 104may include but is not limited to, for example, a process selectiondevice 104 or cluster of computing devices in a first location and asecond computing device or cluster of computing devices in a secondlocation. Process selection device 104 may include one or more computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and the like. Process selection device 104 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. Processselection device 104 may be implemented using a “shared nothing”architecture in which data is cached at the worker, in an embodiment,this may enable scalability of system 100 and/or computing device.

Still referring to FIG. 1, process selection device 104 and/or one ormore modules operating thereon may be designed and/or configured toperform any method, method step, or sequence of method steps in anyembodiment described in this disclosure, in any order and with anydegree of repetition. For instance, process selection device 104 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Process selection device104 may perform any step or sequence of steps as described in thisdisclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

With continued reference to FIG. 1, process selection device 104 isdesigned and configured to receive, from a user device 108, analimentary transfer request. User device 108 may include any device ordevices suitable for use as process selection device 104, as describedabove. An “alimentary transfer request,” as used in this disclosure, isan element of data describing an alimentary collation that a user wishesto have physically transferred to the user from a location other thanlocation at which the user is currently located, by action of a physicalperformance entity 144. As used herein, an “alimentary collation” is aphysical item including at least a first alimentary element, and atleast one of (1) at least an act of preparation of the at least a firstalimentary element and/or (2) at least a second alimentary element. As anon-limiting example, first alimentary element may include aningredient, such as an ingredient in a dish, at least an act ofpreparation may include cooking, dressing, marinating, and/or any otherfood or beverage preparation process for preparing the dish, and atleast a second alimentary element may include an additional ingredientin the dish, which may include flavoring, seasoning, sauces, and/or anycomestible product that may be used as an ingredient in a dish. Analimentary collation may include, without limitation, a restaurant meal,a frozen or otherwise pre-made meal, or a set of ingredients to make ameal. An alimentary transfer request includes at least a description ofan alimentary collation; at least a description may include, withoutlimitation, an allusion to a label identifying the alimentary collation,to one or more data identifying elements of the alimentary collation,and/or a full description of all elements and/or preparationinstructions and/or descriptions of or pertaining to the alimentarycollation. An alimentary transfer request includes at least a terminallocation, where a “terminal location,” as used in this disclosure, isdefined as a location to which an alimentary collation is to bephysically transferred; as a non-limiting example, an alimentarytransfer request may include, as a terminal location, a current orlikely future location of a user submitting the request, and adescription of a dish, meal, set of ingredients, or the like which theuser is requesting be physically transferred to the terminal location. A“physical performance entity 144,” as described herein, is an entitythat performs a physical transfer of an alimentary collation asdescribed above; physical transfer may be effected using pedestrianand/or vehicle delivery, parcel delivery, or any other process forphysically transferring an alimentary collation from a first location toa second location that may occur to a person skilled in the art uponreviewing the entirety of this disclosure.

In an embodiment, and still referring to FIG. 1, process selectiondevice 104 may be configured to receive an alimentary transfer requestby receiving data making up the alimentary transfer request from userdevice 108. For instance, user device 108 may be provided with a userinterface including, without limitation, listing of potential alimentarycollations and/or ingredients therein; listing may be interactive, forinstance permitting one or more data inputs selecting and/or referringto list elements. As a non-limiting example, a list presented to usermay include a menu listing partially or wholly pre-designed alimentarycollations, a menu listing options such as ingredients and/orpreparation instructions to be combined into one or more alimentarycollations upon selection by a user, or the like. A user interface mayalternatively or additionally provide one or more options a user mayselect for physical transfer processes, including terminal locations,desired arrival times at terminal locations, or the like. User interfacemay be provided, without limitation by process selection device 104 and,an origin point device 112, and/or a device, which may be any devicesuitable for use as a user device 108, operated by a physical transferentity, where an “origin point device 112” is a device operated by anentity that assembles or prepares alimentary collations; origin pointdevice 112 may include any device suitable for use as a user device 108.A user may supply a textual description of alimentary transfer requestand/or any element of and/or referred to by alimentary transfer requestto process selection device 104 and/or an origin point device 112;textual description may be analyzed and/or compared to other data usinga language processing module as described in further detail below. Auser may alternatively or additionally place a telephone call or anin-person request with one or more entities operating a device such asuser device 108.

Continuing to refer to FIG. 1, process selection device 104 may beconfigured to receive an alimentary instruction set from aconstitutional guidance system 116. As used in this disclosure, a“constitutional guidance system 116” is a system that generates analimentary instruction set. Constitutional guidance system 116 mayoperate on and/or be incorporated in process selection device 104;alternatively or additionally, constitutional guidance system 116 may beor operate on a remote device in communication with process selectiondevice 104, for instance over a network such as the Internet. An“alimentary instruction set” as used in this disclosure is a datastructure describing one or more alimentary suggestions provided to auser for constitutional guidance and intended to achieve an ameliorativeresult. Alimentary instruction set may, as a non-limiting example,contain instructions one or more daily, weekly, or other periodicdietary or nutritional needs, for instance as determined by a diagnosticprocess, to address one or more health-related issues such as conditionsrequiring specialized alimentary consumption patterns such as diabetes,celiac disease or the like, food allergies, weight-loss or other healthgoals, or the like. Alimentary instruction set may include instructionslisting meals, foods, food groups, ingredients, supplements and the likethat may be compatible with at least a dietary request and or diagnosticprocess; alimentary instruction set may alternatively or additionallylist daily or periodic nutritional goals and/or limits, such as withoutlimitation daily allowances and/or recommendations of salt, sugar, fat,saturated fat, protein, dietary fiber or the like. For example,alimentary instruction set may include a list of three possible mealsthat may be compatible with at least a dietary request for a dairy freediet. In yet another non-limiting example, alimentary instruction setmay include food groups compatible with at least a dietary request suchas a dietary request for a paleo diet may include recommendations as tofood groups that are compatible including meats, fish, poultry, fats,vegetables, and fruits. For example, at least a dietary request and/ordiagnostic result containing a request for a dairy free diet may beutilized to generate an alimentary instruction set that includes asuggestion for breakfast that includes oatmeal topped with coconut milk.In yet another non-limiting example, at least a dietary request for avegetarian diet may be utilized to generate an alimentary instructionset that includes a meal containing tofu, spinach, and rice. In anembodiment, alimentary instruction set generator module may beconfigured to modify alimentary instruction set as a function of the atleast a user entry as described in more detail below. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various elements that may be included in an alimentary instructionset as described in this disclosure.

In an embodiment, and still referring to FIG. 1, process selectiondevice 104 may be configured to modify a user-selected alimentarycollation, for instance selected during a user entry of an alimentarytransfer request as described above, as a function of an alimentaryinstruction set. In an embodiment, process selection device 104 maycompare elements of an alimentary collation selected by a user to one ormore collations and/or ingredients listed in alimentary instruction set;in an embodiment, the selected alimentary collation may unambiguouslylist ingredients and/or collations that may directly be compared toingredients and/or collations listed in alimentary instruction set.Alternatively or additionally, process selection device 104 may comparerequested alimentary collation to alimentary instruction set through useof one or more additional elements. For instance, and withoutlimitation, system 100 may include a collation element database 120linking alimentary collations to component elements. A collation elementdatabase 120 may include any data structure for ordered storage andretrieval of data, which may be implemented as a hardware or softwaremodule. A collation element database 120 may be implemented, withoutlimitation, as a relational database, a key-value retrieval datastoresuch as a NOSQL database, or any other format or structure for use as adatastore that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. A collation elementdatabase 120 may include a plurality of data entries and/or recordscorresponding to alimentary collations as described above. Data entriesand/or records may describe, without limitation, data describing one ormore potential ingredient sets and/or preparation processes associatedwith an alimentary collation. Data entries in a collation elementdatabase 120 may be flagged with or linked to one or more additionalelements of information, which may be reflected in data entry cellsand/or in linked tables such as tables related by one or more indices ina relational database; one or more additional elements of informationmay include data describing regional, geographic, and/or other extantvariations in ingredients and/or preparation techniques relating tolisted alimentary collations. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various ways in whichdata entries in a collation element database 120 may reflect categories,cohorts, and/or populations of data consistently with this disclosure.

Referring now to FIG. 2, one or more database tables in collationelement database 120 may include, as a non-limiting example, a collationtable 200; collation table 200 may list a plurality of collation names,and links to ingredients and/or processes involved in generating eachcollation. one or more database tables in collation element database 120may include, as a non-limiting example, an ingredient table 204;ingredient table may list a plurality of ingredients that may beincluded in collations as listed in collation table 200. One or moredatabase tables in collation element database 120 may include, as anon-limiting example, a collation process table 208; collation processtable 208 may list processes for generation of collations, includingwithout limitation cooking and other preparation procedures.

Referring again to FIG. 1, process selection device 104 may comparealimentary transfer request to alimentary instruction set by comparingone or more elements of textual data in alimentary transfer request toone or more elements of textual data in alimentary instruction set;comparison may be performed, for instance, using keyword matching and/orother forms of string comparison. Alternatively or additionally, textualcomparison may be performed using a language processing module. Languageprocessing module may include any hardware and/or software module.Language processing module may be configured to extract, from the one ormore documents, one or more words. One or more words may include,without limitation, strings of one or characters, including withoutlimitation any sequence or sequences of letters, numbers, punctuation,diacritic marks, engineering symbols, geometric dimensioning andtolerancing (GD&T) symbols, chemical symbols and formulas, spaces,whitespace, and other symbols, including any symbols usable as textualdata as described above. Textual data may be parsed into tokens, whichmay include a simple word (sequence of letters separated by whitespace)or more generally a sequence of characters as described previously. Theterm “token,” as used herein, refers to any smaller, individualgroupings of text from a larger source of text; tokens may be broken upby word, pair of words, sentence, or other delimitation. These tokensmay in turn be parsed in various ways. Textual data may be parsed intowords or sequences of words, which may be considered words as well.Textual data may be parsed into “n-grams”, where all sequences of nconsecutive characters are considered. Any or all possible sequences oftokens or words may be stored as “chains”, for example for use as aMarkov chain or Hidden Markov Model.

Still referring to FIG. 1, language processing module may operate toproduce a language processing model. Language processing model mayinclude a program automatically generated by process selection device104 and/or language processing module to produce associations betweenone or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations of extracted words withcategories of physiological data, relationships of such categories toprognostic labels, and/or categories of prognostic labels. Associationsbetween language elements, where language elements include for purposesherein extracted words or phrases, including words or phrases labelingand/or describing one or more alimentary collations and/or elementsthereof, include, without limitation, mathematical associations,including without limitation statistical correlations between anylanguage element and any other language element and/or languageelements. Statistical correlations and/or mathematical associations mayinclude probabilistic formulas or relationships indicating, forinstance, a likelihood that a given first word or phrase is synonymouswith and/or typically associated with a given second word or phrase. Asa further example, statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating a positive and/or negative association between at least anextracted word or phrase and a given alimentary element and/orcollation; positive or negative indication may include an indicationthat a given document is or is not indicating a given alimentary elementand/or collation, and/or that the relationship between the word orphrase and the given alimentary element and/or collation is or is notsignificant. For instance, inclusion of a negating term such as “not” ina sentence correlating a first word with a second word may be analyzedto determine whether that correlation constitutes a positive or negativecorrelation; whether a phrase, sentence, word, or other textual elementin a document or corpus of documents constitutes a positive or negativeindicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat process selection device 104, or the like.

Still referring to FIG. 1, language processing module and/or processselection device 104 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module maycombine two or more approaches. For instance, and without limitation,machine-learning program may use a combination of Naive-Bayes (NB),Stochastic Gradient Descent (SGD), and parameter grid-searchingclassification techniques; the result may include a classificationalgorithm that returns ranked associations.

Continuing to refer to FIG. 1, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1, language processing module may use a corpusof documents to generate associations between language elements in alanguage processing module, and process selection device 104 may thenuse such associations to analyze words and/or phrases extracted from oneor more documents and determine statistical relationships between suchwords and/or phrases. Corpus of documents may include, withoutlimitation, any set of textual information described above foralimentary transfer requests and/or alimentary instruction sets,including without limitation menus, descriptions of alimentarycollations previously prepared and/or listed on menus, descriptions ofone or more alimentary elements and/or processes, or the like. Corpus ofdocuments may include articles, web pages, books and/or book excerpts,or any other source of text associating words and/or phrases asdescribed above to one another. Documents may be entered into processselection device 104 by being uploaded by an expert or other personsusing, without limitation, file transfer protocol (FTP) or othersuitable methods for transmission and/or upload of documents;alternatively or additionally, where a document is identified by acitation, a uniform resource identifier (URI), uniform resource locator(URL) or other datum permitting unambiguous identification of thedocument, such as an international standard book number (ISBN), processselection device 104 may automatically obtain the document using such anidentifier, for instance by submitting a request to a database orcompendium of documents such as JSTOR as provided by Ithaka Harbors,Inc. of New York.

With continued reference to FIG. 1, process selection device 104 maymodify user-selected alimentary transfer request by comparing arequested alimentary collation from selected alimentary transfer requestto one or more instructions in alimentary instruction set anddetermining that at least an element of requested alimentary collationviolates one or more instructions of alimentary instruction set. Forinstance, alimentary instruction set may establish a maximal intakeamount of a given nutrient, such as an upper limit on fat or sugarconsumption, and requested alimentary collation may include an elementand/or ingredient that exceeds that maximal intake amount; for instance,the requested alimentary collation may include a meal having a largerfat content than permitted by alimentary instruction set. In such asituation, process selection device 104 may modify alimentary transferrequest to list an alimentary collation that follows the violatedalimentary instruction or instructions; a recipe may be modified, forinstance, or a first meal description may be replace with a second mealdescription that complies with alimentary instruction set. Alternativelyor additionally, one or more alternative and/or modified alimentarycollations may be presented to the user, via user device 108 or thelike, along with an entry field such as a checkbox and/or button wherebythe user may select one of the presented alternatives. User may bepresented with the ability to refuse modification; for instance, usermay be provided with an entry field permitting the user to proceed withan unmodified alimentary transfer request.

Alternatively or additionally, and continuing to refer to FIG. 1,process selection device 104 may be configured to receive an alimentarytransfer request by receiving, from a constitutional guidance system 116an alimentary instruction set and generate the alimentary transferrequest as a function of the alimentary instruction set; alimentaryinstruction set may include any alimentary instruction set as describedabove. For instance, and without limitation, alimentary instruction setmay be transmitted to a user via a graphical user interface coupled touser device 108. User may select at least an alimentary instruction fromalimentary instruction set by selecting one or more nutritional goalsdescribed by the one or more instructions; selection of one or moregoals may cause generation of at least a matching alimentary collationand/or ingredient, for instance by reference to collation elementdatabase 120 as described above. As an illustrative and non-limitingexample, user may select instructions associated with consuming acertain quantity of vegetables, protein, and carbohydrates, as suggestedin alimentary instruction set; each selection may prompt generation of adrop-down list of ingredients matching and/or fulfilling the vegetable,protein, and carbohydrate requirements, or selection of all three may bematched to an alimentary collation that is listed in collection elementdatabase as fulfilling all three. Selection of individual ingredientsmay be used to generate one or more matching alimentary collations,which may be performed by using such selections to create a query forcollation element database 120, producing a result set representing oneor more alimentary collations. In an embodiment, a plurality ofalimentary collations generated as described above may be presented touser, permitting user to select an alimentary collation from theplurality. A plurality of alimentary collations may be filtered bycomparison of alimentary collations to user history; for instance,alimentary collations frequently and/or previously selected by user maybe displayed, where “frequently” selected elements may include elementsthat have been selected by user more than a threshold number of times inthe past, and/or elements that have been selected by the user more thana threshold number of times in a recent period such as a month, quarter,year, or the like

Still referring to FIG. 1, history of previous user interactions withsystem 100 may be stored in and/or retrieved from a user historydatabase 124. User history database 124 may include any database, datastore, and/or data structure suitable for use as collection elementdatabase as described above.

Referring now to FIG. 3, an exemplary embodiment of a user historydatabase 124 is illustrated. One or more tables in user history database124 may include a constraint history table 300, which may list pastcategorical constraints as described in further detail below. Userhistory database 124 may include a collation history table 304, whichmay list collations previously selected by user according to methodsand/or systems as described herein. User history database 124 mayinclude a physical transfer history table 308, which may list one ormore physical transfers, as described in further detail below,previously selected by a user.

Alternatively or additionally, and referring again to FIG. 1, processselection device 104 may receive a user request to generate one or morealimentary transfer requests automatically, and automatically generatethe one or more alimentary transfer requests. Automatic generation ofone or more alimentary transfer requests may be performed by retrievingone or more previously selected alimentary transfer requests from userhistory database 124. Alternatively or additionally, automaticgeneration of one or more alimentary transfer requests may be performedusing an alimentary instruction set; for instance, one or more suggestedor potential alimentary collations may be provided with alimentaryinstruction set, and/or generated using one or more sets of ingredientsspecified by and/or meeting instructions of alimentary instruction set.A combination of methods may be used; for instance, process selectiondevice 104 may retrieve a list of potential alimentary collations fromalimentary instruction set, based on, for instance the time of day,instructions previously fulfilled by the user, or the like, which maythen be filtered and/or ranked according to user history as retrievedfrom user history database 124. As a further non-limiting example, aninitial set of alimentary collations may be listed in user history asretrieved from user history database 124 and may be filtered accordingto compliance with alimentary instruction set.

Still referring to FIG. 1, additional elements of alimentary transferrequest may be generated automatically. For instance, history of pastuser selections may be used to generate a probable terminal location,which may be set as a terminal location of an alimentary transferrequest, and/or presented to a user via user device 108 forconfirmation. As a further non-limiting example, a user's current and/orintended location may be received from user device 108, for instance asdetermined by map programs, satellite navigation facilities such as theGlobal Positioning System (GPS), cell tower contact and/ortriangulation, or the like; for example, user may be navigatingaccording to turn-by-turn directions to a particular location, which maybe selected as terminal location. User locations received from userdevice 108 may be compared to past terminal locations; thus, wherecurrent or intended further user location is ambiguous and/or determinedonly to a low level of accuracy, user history may be matched to detecteduser location to determine a probable terminal location; any terminallocation automatically determined by process selection device 104 may bepresented to a user for confirmation.

Continuing to FIG. 1, process selection device 104 may be configured toprovide at least categorical constraint modifying the alimentarytransfer request. A “categorical constraint,” as used herein, is anyconstraint limiting a choice of an alimentary collation and/or anelement thereof. At least a categorical constraint may include at leasta constitutional restriction, defined as any constitutional reason thata user may be unable to engage in an alimentary instruction set process,consume a particular alimentary element, and/or consume a particularalimentary collation; at least a constitutional restriction may includea contraindication such as an injury, a diagnosis such as by an informedadvisor including a functional medicine doctor, an allergy or foodsensitivity issue, a contraindication due to a medication or supplementthat a user may be taking. For example, a user diagnosed with ahypercholesteremia and currently taking a cholesterol loweringmedication such as a statin may report a constitutional restriction thatincludes an inability to consume grapefruit containing foods and foodproducts. At least a constitutional restriction may include arestriction to a diet free of shellfish because of a user's IgE allergicresponse to shellfish that was diagnosed when a user was a little child.At least a constitutional restriction may include a restriction to acertain diet because of a previously diagnosed medical condition, suchas a user who has been previously diagnosed with Candida and isfollowing a low sugar diet. At least a constitutional restriction mayinclude a constitutional restriction as a function of a medication,supplementation, and/or medical treatment or therapy that a user may beundergoing. For example, a user currently taking a medication such asmetronidazole may generate at least a constitutional restriction to analcoholic free diet.

Alternatively or additionally, and still referring to FIG. 1, at least acategorical restriction may include a user preference. A user preferencemay include a request for a particular diet, food, food group, nutritionplan, style of eating, lifestyle, and/or nutrition. At least a userpreference may include a request for a particular type of diet such asAtkins, Paleo, Whole 30, gluten free, ketogenic, dairy free,Mediterranean, soy free, and the like. At least a user preference mayinclude elimination of certain foods or food groups because of a dislikefor such foods, an allergy to a food, and/or a sensitivity. For example,at least a user preference may include a request for an egg free dietbased on a user's aversion to eggs. In yet another non-limiting example,at least a user preference may include a request for a diet free of bellpeppers because of a user's previous IgG food sensitivity testing. Atleast a user preference may include a request for a diet based onreligious or moral beliefs such as kosher diet or vegetarian diet. Atleast a user preference may include a request to eliminate certain foodgroups such as a nightshade free diet or a grain free diet. At least auser preference may include a request to eliminate certain ingredientsthat may be commonly found in food such as a request for a diet free ofmonosodium glutamate (MSG) or corn starch. At least a user preferencemay include a request for a certain level or quality of ingredients suchas locally sourced ingredients, free range meats, wild caught fish,organic produce and the like. At least a user preference may include auser preference based on a certain style of eating that a user prefers,such as low carb, high protein, low fat, and the like.

With continuing reference to FIG. 1, providing the at least acategorical constraint may include receiving the at least a categoricalconstraint from a user device 108. For instance, and without limitation,user may enter, via a user interface provided on user device 108, one ormore user preferences and/or dietary restrictions; as a non-limitingexample, the user may be aware that the user is allergic to a particularcategory of alimentary elements such as nuts, fish, or the like, and mayenter that information via user device 108. Alternatively oradditionally, providing at least a categorical constraint may includereceiving, from a constitutional guidance system 116 an alimentaryinstruction set and generating the at least a categorical constraint asa function of the alimentary instruction set. For instance,constitutional guidance system 116 may determine based on diagnostictesting and/or procedures that user is diabetic and add to alimentaryinstruction set that user should not consume sugars. In an embodiment, adietary restriction may be received from the user, sent to the healthguidance system, and used by the health guidance system to generate thenutrition plan. One or more categorical constraints may be stored inand/or retrieved from a user constraint database 128; for instance,providing at least a categorical constraint may include retrieval of atleast a categorical constraint from a user constraint database 128. Userconstraint database 128 may include any database, data store, and/ordata structure suitable for use as a collation element database 120 asdescribed above.

Referring now to FIG. 4, one or more tables in user constraint database128 may include a constitutional restriction table 400, which may beused to list constitutional restrictions, as described above, associatedwith user. Constitutional restriction table may, for instance,

Referring again to FIG. 1, system 100 may include a dietary datadatabase 132; dietary database may include any database or datastoresuitable for use as collation element database. In an embodiment,dietary database may list foods compatible with one or more categoricalconstraints as described below.

Referring now to FIG. 3, one or more database tables in dietary datadatabase 132 may include, as a non-limiting example, a compatible foodstable 300. For instance and without limitation, compatible foods table300 may be a table relating categorical constraints to foods that arecompatible with a particular categorical constraint as described infurther detail below; for instance where a categorical constraintcontains a request for a ketogenic diet foods such as beef tips, groundsirloin and lamb shanks may be compatible with such a request while suchfoods may not be compatible with a categorical constraint for a vegandiet. Dietary data database 132 may include moderately compatible foodtable 304 which may be a table relating categorical constraint to foodsthat are moderately compatible with a particular categorical constraint;for instance where a categorical constraint contains a request for agluten free diet from a user with a self-reported gluten intolerance,foods such as certified gluten free oats may be moderately compatiblewith such a user, while certified gluten free oats may not be compatiblefor a user following a gluten free diet because of a previous diagnosisof Celiac Disease. For instance and without limitation, dietary datadatabase 132 may include as a non-limiting example, incompatible foodtable 308. For instance and without limitation, incompatible food table308 may include a table relating categorical constraints to foods thatare incompatible with a particular categorical constraint; for instancewhere a categorical constraint contains a request for a corn free dietingredients such as cornstarch, corn oil, dextrin, maltodextrin,dextrose, fructose, ethanol, maize, and/or sorbitol may be listed. In anembodiment, database tables contained within dietary data database 132may include groupings of foods by different categories such as grains,meats, vegetables, fruits, sugars and fats, and the like. In anembodiment, database tables contained within dietary data database 132may include groups of foods by ingredients that a food may be comprisedof, for example gravy may contain flour which may contain gluten.

Referring again to FIG. 1 system 100 includes a descriptor generatormodule 136 operating on the process selection device 104. Descriptorgenerator module 136 may include any suitable hardware and/or softwaremodule as described in this disclosure. Descriptor generator module 136is designed and configured to generate a plurality of alimentarytransfer descriptors. An alimentary transfer descriptor is an element ofdata describing (1) an alimentary collation as producible by an entityphysically capable of producing alimentary collations, and (2) at leasta parameter of a physical transfer process bringing the alimentarycollation to the terminal location specified in a correspondingalimentary transfer request. Each alimentary transfer descriptordescribes a physical transfer process, of a plurality of physicaltransfer processes, to be performed by a corresponding physicalperformance entity 144 of a plurality of physical performance entities.Each alimentary transfer descriptor describes an alimentary collation tobe provided during a corresponding physical transfer process. As anon-limiting example, generating the plurality of alimentary transferdescriptors may include receiving, an alimentary collation descriptorfrom an origin point device 112 as described above, and matching, by thedescriptor generator module 136, the alimentary collation descriptor tothe alimentary transfer request. Matching may include, withoutlimitation, exact matching; for instance, where origin point device 112provides an alimentary collation descriptor that is recorded incollation element database 120, such an alimentary collation descriptormay precisely match an alimentary transfer request. Origin point device112 s and/or entities operating them may, for instance, provideingredient lists to system 100 to permit exact matching. As a furthernon-limiting example, an origin point device 112 and/or a menu mayprovide a precise list of ingredients, enabling exact matching of suchingredients to alimentary elements associated in collation elementdatabase 120 with an alimentary collation in an alimentary transferrequest. As an additional non-limiting example, provided alimentarycollation descriptor may match an alimentary collation previouslyrequested and/or received by user; such a previously requested and/orreceived alimentary collation may be recorded in collation elementdatabase 120 and/or user history database 124.

Alternatively or additionally, and still referring to FIG. 1, descriptorgenerator module 136 may perform one or more estimated and/or inexactmatching processes to identify an alimentary collation offered by anorigin point device 112 that matches an alimentary collation ofalimentary transfer request. Estimated matching may be performed,without limitation, by matching one or more names and/or labels ofalimentary collation descriptor received from origin point device 112with a name of an alimentary collation of alimentary transfer request;matching may be performed using natural language module and/or one ormore processes effected thereby, including without limitation vectorsimilarity matching. As a further example, a list of ingredients of analimentary collation received from an origin point device 112 may becompared to lists of ingredients retrieved from collation elementdatabase 120 to detect alimentary collations from the database havingmore than a threshold number of ingredients in common with the list ofingredients. In an embodiment, alimentary collations received fromorigin point device 112 s may be maintained in an alimentary collationdata store 140. For instance, and without limitation, origin pointdevice 112 s may transmit to system 100 alimentary collation descriptorsand/or associated ingredients, which may be stored in alimentarycollation data store 140. Entries in alimentary collation data store 140may be modified, for instance so that names of ingredients are changedto synonymous names from collation element database 120, for instance asdetected using language processing module. Generation of descriptors maytherefore include querying alimentary collation data store 140 using atleast an alimentary transfer request. Origin point device 112 s and/oralimentary collation data store 140 may include temporal limits, such asindications of times of day, days of the week, and/or dates during whicha given alimentary collation is available. Alternatively oradditionally, descriptor generator module 136 may transmit, to an originpoint device 112, an alimentary transfer request.

With continued reference to FIG. 1, descriptor generator module 136 maygenerate one or physical transfer processes by communicating with one ormore physical performance entities. For instance, and withoutlimitation, descriptor generator module 136 may transmit, to eachphysical performance entity 144 of a plurality of physical performanceentities, information describing the alimentary transfer request;information may include, without limitation, an origin point andterminal point. Each physical performance entity 144 may transmit toprocess selection device 104, path information for one or more vehiclesof physical performance entity 144. Path information may include,without limitation, a current location of a vehicle, a current headingor direction of travel of the vehicle, one or more future stops at whichthe vehicle is currently scheduled to stop, and/or one or more paths thevehicle is likely to travel. In an embodiment, physical performanceentity 144 may receive only a request to describe such vehicles, and maysend only such path information; alternatively, where descriptorgenerator module 136 has transmitted origin point and/or terminal point,physical performance entity 144 may send one or more potential pathsthat one or more vehicles may be able to traverse between origin pointand terminal point. In an embodiment, prospective paths may becalculated based on estimated time of production of an alimentarycollation; for instance a collation may take 20 minutes from request tobe completed, and physical performance entity 144 may estimate likelyability of one or more vehicles to arrive at or near to the time ofcompletion. In an embodiment, time until completion of an alimentarycollation is provided by an origin point device 112 corresponding to theentity preparing the alimentary collation; alternatively oradditionally, description generator may determine an average amount oftime a given alimentary collation takes to be completed at a givenentity, and use the determined average amount of time to determine alikely time of completion.

Alternatively or additionally, and still viewing FIG. 1, descriptorgenerator module 136 136 may generate estimated paths for one or morevehicles given report positions for each of the one or more vehicles,origin point, and terminal location; persons skilled in the art will beaware of various ways in which estimated paths may be generated. Agenerated path may depend on another generated path; for instance, apotential or selected path from a first origin point to a first terminalpoint may include at least a segment of a path from a second originpoint to a second terminal point, and descriptor generator module 136136 may generate potential paths for each assuming that the other pathhas or has not been chosen. As a non-limiting example, where topotential paths having shared segments are presented to two or moreusers sharing a terminal point, descriptor generator module 136 136 mayprovide the two users with at least an alimentary transfer descriptorincluding a shared route as a possible selection for the two users.

Continuing to refer to FIG. 1, generation of each alimentary transferdescriptor of at least an alimentary transfer descriptor may includecombination of an alimentary collation of at least an alimentarycollation identified as described above with a physical transfer processof at least a physical transfer process; this may generate a largenumber of alimentary transfer descriptors, as each alimentary collationmay have multiple potential paths for its delivery, and there may be alarge quantity of alimentary collations that may potentially matchalimentary transfer request. This initial set of candidates may bechosen within a geographical/travel time threshold; for instance, anypath having a length above a threshold amount, a travel time above athreshold amount, and/or a length and/or time exceeding an averagetravel time and/or length of paths generated by more than a thresholdamount. Similarly, origin points more than a threshold geographicaldistance and/or travel time away from terminal point may be eliminatedfrom generated alimentary transfer descriptors. Each threshold may beestablished as a default value and/or selected by a user; any thresholdmay be user specific.

Still referring to FIG. 1, each alimentary transfer descriptor furtherincludes a plurality of attributes, each attribute corresponding to aselection criterion of a plurality of selection criteria. Each attributemay include a degree of variance from an element of alimentary request,which element may be described here for the sake of brevity as a“requested element”; for instance, a first attribute may represent adegree to which an ingredient list differs from a requested ingredientlist, a higher number representing a greater number of divergentingredients, while at least second attribute may represent a degree towhich an ingredient list differs from the at least a categoricalconstraint, and a third attribute may represent a degree to which aphysical transfer process of an alimentary transfer descriptor differsfrom an ideal or optimal travel distance of time. Each degree ofvariance may include either a degree to which a provided value differsfrom the requested value, a degree to which the exact variance isunknown, or both; for instance, where not all ingredients are known, adegree of variance from requested ingredients may be estimated based ona typical recipe, and/or assumed to be high. Similarly, a routecalculated as a physical transfer process that has a certain traveltime, but also a certain degree of uncertainty in that travel time, maybe calculated as having a higher degree of variance than a route havinga lesser degree of uncertainty from the same travel time. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various attributes that may be calculated or derived withregard to various elements of alimentary transfer descriptors ascompared to various elements of alimentary transfer requests; generally,an attribute may be generated to reflect a degree of variance from anyoption a user may select regarding any alimentary transfer request.

Continuing to refer to FIG. 1, system 100 includes a selector module 148operating on the process selection device 104. Selector module 148 mayinclude any suitable hardware module and/or software module as describedin this disclosure. Selector module 148 designed and configured toselect an alimentary transfer descriptor of the plurality of alimentarytransfer descriptors by executing a selection function on the pluralityof alimentary transfer descriptors. As used herein, a “selectionfunction” is a function that generates a selection output as a functionof the plurality of selection criteria, the plurality of attributes, andthe categorical constraint; a “selection output” as used herein is anoutput that orders a plurality of alimentary transfer descriptorsaccording to a degree of desirability or optimization given selectioncriteria. As a non-limiting example, selection function may rank allalimentary transfer descriptors according to each attribute, calculatean average ranking across attributes per alimentary transfer descriptor,where average may include any form of average including withoutlimitation arithmetic mean and/or geometric mean, and/or rank allalimentary transfer descriptors according to attribute-wise rankingand/or average ranking. Selection function may, for instance displayonly a highest-ranking option or a certain number of highest-rankingoptions to a user; alternatively, selection function may display aplurality, or all options with rankings according to one or morecriteria, ordered by average ranking, one or more per-criteria rankings,or any other ranking as described in this disclosure. A user may be ableto select which ranking according to which alimentary transferdescriptors are ranked, or a ranking may be selected by default.

In an embodiment, selection function used by process selection device104 may compare one or more alimentary transfer descriptors to amathematical expression representing an optimal combination ofalimentary provision parameters. Mathematical expression may include alinear combination of parameters, weighted by coefficients representingrelative importance of each parameter in generating an optimalalimentary transfer descriptor. For instance, a total transit time inseconds of an alimentary transfer descriptor may be multiplied by afirst coefficient representing the importance of total transit time, atotal cost of an alimentary transfer descriptor may be multiplied by asecond coefficient representing the importance of cost, a degree ofvariance from an alimentary instruction set may be represented asanother parameter, which may be multiplied by another coefficientrepresenting the importance of that parameter, a degree of variance froma requested recipe may be multiplied by an additional coefficientrepresenting an importance of that parameter, and/or a parameterrepresenting a degree of variance from one or more dietary restrictionsmay be provided a coefficient representing the importance of such avariance; persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various parameters that may beweighted by various coefficients. Use of a linear combination isprovided only as an illustrative example; other mathematical expressionsmay alternatively or additionally be used, including without limitationhigher-order polynomial expressions or the like.

Still viewing FIG. 1, mathematical expression may represent a costfunction, where a “cost function” is an expression an output of which anoptimization algorithm minimizes to generate an optimal result. As anon-limiting example, process selection device 104 may calculateparameters of each of a plurality of alimentary transfer descriptors,calculate an output of mathematical expression using the parameters, andselect an alimentary transfer descriptor that produces an output havingthe lowest size, according to a given definition of “size,” of the setof outputs representing each of the plurality of alimentary transferdescriptors; size may, for instance, included absolute value, numericalsize, or the like. Selection of different cost functions may result inidentification of different alimentary transfer descriptors asgenerating minimal outputs; for instance, where transit time isassociated in a first cost function with a large coefficient or weight,an alimentary transfer descriptor having a short transit time mayminimize the first cost function, whereas a second cost function whereintransit time has a smaller coefficient but degree of variance from adietary restriction has a larger coefficient may produce a minimaloutput for a different alimentary transfer descriptor having a longertransit time but more closely hewing to a dietary restriction.

Alternatively or additionally, and still referring to FIG. 1, eachalimentary transfer descriptor may be represented by a mathematicalexpression having the same form as mathematical expression; processselection device 104 may compare the former to the latter using an errorfunction representing average difference between the two mathematicalexpressions. Error function may, as a non-limiting example, becalculated using the average difference between coefficientscorresponding to each parameter. An alimentary transfer descriptorhaving a mathematical expression minimizing the error function may beselected, as representing an optimal expression of relative importanceof parameters to a system or user. In an embodiment, error function andcost function calculations may be combined; for instance, an alimentarydelivery option resulting in a minimal aggregate expression of errorfunction and cost function, such as a simple addition, arithmetic mean,or the like of the error function with the cost function, may beselected, corresponding to an option that minimizes total variance fromoptimal parameters while simultaneously minimizing a degree of variancefrom a set of priorities corresponding to alimentary transfer descriptorparameters. Coefficients of mathematical expression and/or cost functionmay be scaled and/or normalized; this may permit comparison and/or errorfunction calculation to be performed without skewing by varied absolutequantities of numbers.

Still referring to FIG. 1, mathematical expression and/or cost functionmay be provided by receiving one or more user commands. For instance,and without limitation, a graphical user interface may be provided touser with a set of sliders or other user inputs permitting a user toindicate relative and/or absolute importance of each parameter to theuser. Sliders or other inputs may be initialized prior to user entry asequal or may be set to default values based on results of anymachine-learning processes or combinations thereof as described infurther detail below.

With continued reference to FIG. 1, mathematical expression and/or costfunction may be generated using a machine learning to produce costfunction. For instance, and without limitation, a linear regressionprocess may be performed to generate a linear function of attributes tobe used as a loss function as described above. A machine learningprocess, as used herein is a process that automatedly uses a body ofdata known as “training data” and/or a “training set” to generate analgorithm that will be performed by a computing device/module to produceoutputs given data provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage.

Still referring to FIG. 1, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 152 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 152 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 152 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 152 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 152 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 152 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data152 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1, trainingdata 152 may include one or more elements that are not categorized; thatis, training data 152 may not be formatted or contain descriptors forsome elements of data. Machine-learning algorithms and/or otherprocesses may sort training data 152 according to one or morecategorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name and/or a description of amedical condition or therapy may be identified by reference to a list,dictionary, or other compendium of terms, permitting ad-hoccategorization by machine-learning algorithms, and/or automatedassociation of data in the data entry with descriptors or into a givenformat. The ability to categorize data entries automatedly may enablethe same training data 152 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow.

In an embodiment, and continuing to refer to FIG. 1, training data 152may be created using a plurality of past user interactions. Past userinteractions may include only interactions with a current user; in thiscase loss function may be user-specific, using a training set composedof past user selections. Such a user-specific training set may initiallybe seeded using one or more user entries as above. Similarly, user mayenter a new command changing mathematical expression, and thensubsequent user selections may be used to generate a new training set tomodify the new expression. Alternatively or additionally, training data152 may include past interactions with one or more additional users. Oneor more additional users may be selected based on similarities tocurrent user; similarities may include, without limitation, one or moresimilar medical conditions, one or more similar dietary restrictions,one or more demographic similarities, such as age, sex, ethnicity,national origin, language, or the like. As a non-limiting example,selector module 148 may search user history database 124 for usershaving at least a categorial constraint matching at least a categoricalconstraint of current user and use history of such users to generatetraining data 152; this process may alternatively or additionally beused to select all users sharing any other attribute or demographicfeature with current user in user history database 124 and generatetraining data 152 based on such users' histories. Alternatively oradditionally, all users may be used. In an embodiment, selector module148 may generate a machine-learning model using all users or a selectedset of users as described above, and then modify the model usingadditional training using history of current user.

Still referring to FIG. 1, selector module 148 may be designed andconfigured to create at least a machine-learning model relating inputsrepresenting attributes to a loss function output as described above;for instance, machine-learning model pay present a linear or othermathematical combination of attribute values with weights or otherexpressions indicating relative importance within the linear or othermathematical combination. Such models may include without limitationmodel developed using linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure. Machine-learning may include otherregression algorithms, including without limitation polynomialregression.

Continuing to refer to FIG. 1, machine-learning algorithm used togenerate machine-learning model may include, without limitation, lineardiscriminant analysis. Machine-learning algorithm may include quadraticdiscriminate analysis. Machine-learning algorithms may include kernelridge regression. Machine-learning algorithms may include support vectormachines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighborsalgorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naive Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 1, selector module 148 may generate lossfunction using alternatively or additional artificial intelligencemethods, including without limitation by creating an artificial neuralnetwork, such as a convolutional neural network comprising an inputlayer of nodes, one or more intermediate layers, and an output layer ofnodes. Connections between nodes may be created via the process of“training” the network, in which elements from a training data 152 setare applied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Referring now to FIG. 6, machine-learning algorithms used by selectormodule 148 may include supervised machine-learning algorithms, whichmay, as a non-limiting example be executed using a supervised learningmodule 600 executing on process selection device 104 and/or on anothercomputing device in communication with process selection device 104,which may include any hardware or software module. Supervised machinelearning algorithms, as defined herein, include algorithms that receivea training set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm may useattributes as inputs, a loss function output as an output, and a scoringfunction representing a desired form of relationship to be detectedbetween elements of physiological data and prognostic labels; scoringfunction may, for instance, seek to minimize the degree of error betweengenerated loss function and results presented in training data 152.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 152. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of supervised machine learningalgorithms that may be used to generate a mathematical function or model604 relating attributes to loss-function outputs.

Still referring to FIG. 1, selector module 148 may alternatively oradditionally be designed and configured to generate at least an outputselecting an by executing a lazy learning process as a function of thetraining data 152 and the alimentary transfer request; lazy learningprocesses may be performed by a lazy learning module 608 executing onprocess selection device 104 and/or on another computing device incommunication with process selection device 104, which may include anyhardware or software module. A lazy-learning process and/or protocol,which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover a “first guess” ata prognostic label associated with physiological test sample, usingtraining data 152. As a non-limiting example, an initial heuristic mayinclude a ranking of prognostic labels according to relation to a testtype of at least a physiological test sample, one or more categories ofphysiological data identified in test type of at least a physiologicaltest sample, and/or one or more values detected in at least aphysiological test sample; ranking may include, without limitation,ranking according to significance scores of associations betweenelements of physiological data and prognostic labels, for instance ascalculated as described above. Heuristic may include selecting somenumber of highest-ranking associations and/or prognostic labels.Selector module 148 may alternatively or additionally implement anysuitable “lazy learning” algorithm, including without limitation aK-nearest neighbors algorithm, a lazy naive Bayes algorithm, or thelike; persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various lazy-learning algorithms that maybe applied to generate prognostic outputs as described in thisdisclosure, including without limitation lazy learning applications ofmachine-learning algorithms as described in further detail below.

In an embodiment, and continuing to refer to FIG. 6, selector module 148may perform more than one cost function and/or lazy learning process;for instance, while a first cost function and/or lazy learning processmay relate all attributes to a selection output, a second cost functionand/or lazy learning process may select a most efficient physicaltransfer route, which in turn may be used to score route efficienciesand be input to a more global cost function, for instance by presentingto the more global cost function a list of alimentary transferdescriptors with optimal routes. Alternatively or additionally, acurrent user may specify one or more attributes according to which thatuser wishes a selection function to be performed, eliminating use ofother attributes to derive and/or perform selection function. Selectionfunction output 612 may be generated by any of the above-describedprocesses, models, and/or modules, or any combination thereof. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which selection functions may be generatedand/or customized to produce selection outputs as described in thisdisclosure.

In an embodiment, and referring again to FIG. 1, selector module 148 isdesigned and configured to select the alimentary transfer descriptorbased on the selection output. Selection may include selection of analimentary transfer descriptor having a highest-ranked selectionfunction output, such as an alimentary transfer descriptor having thelowest loss function output. Selection may include presentation of twoor more alimentary transfer descriptors to a user via user device 108,for instance as described above, and receiving a user selection of oneof the two or more alimentary transfer descriptors; two or morealimentary transfer descriptors may be ranked according to one or moreselection function outputs as described above. Display of alimentarytransfer descriptors may include display of all generated alimentarytransfer descriptors; alternatively or additionally a smaller subset ofthe generated alimentary transfer descriptors may be displayed. Asmaller subset may be selected according to any process for selecting asmaller subset as described herein, including without limitationselection of a number of alimentary transfer descriptions associatedwith highest-ranked or most optimal selection function outputs; forinstance, where selection function is or includes a loss function,selection may include selection of a set of alimentary transferdescriptors having the smallest loss function outputs. A number ofalimentary transfer descriptors having minimal loss function outputsand/or highest ranking selection function outputs may be set accordingto a number stored in memory of process selection device 104; the numbermay be set by a user, who may be permitted to request selection of anynumber of alimentary transfer descriptors to be displayed. Alternativelyor additionally, the number may be set by default; a user entry maymodify number from default to another value.

Still referring to FIG. 1, process selection device 104 is designed andconfigured to transmit a selected alimentary transfer descriptor to aphysical performance entity 144 corresponding to the selected alimentarytransfer descriptor. Transmitting alimentary transfer descriptor to aphysical performance entity 144 may include transmitting sufficient datafor the physical performance entity 144 to perform the correspondingprocess, including without limitation transmitting an element of dataindicating selection of a physical transfer process previously sent fromphysical performance entity 144, transmitting an origin point, terminalpoint, and/or pickup time, or the like. For instance, and withoutlimitation, physical performance entity 144 may generate its ownnavigation directions given an origin point and terminal point.Transmission may further include transmission to an origin point device112, for instance to instruct an entity operating the origin pointdevice 112 to prepare or produce an alimentary collation as instructedby selected alimentary transfer descriptor.

Referring now to FIG. 7, an exemplary embodiment of a method 700 ofselecting an alimentary transfer descriptor using categoricalconstraints is illustrated. At step 705, a process selection device 104receives, an alimentary transfer request including at least adescription of an alimentary collation and at least a terminal location;this may be implemented, for instance, as described above in referenceto FIGS. 1-6. Receiving the alimentary transfer request furthercomprises receiving, from a user device 108, the alimentary transferrequest, for instance as described above in reference to FIGS. 1-6.Process selection device may receive, from a constitutional guidancesystem 116, an alimentary instruction set and modify the alimentarycollation as a function of the alimentary instruction set, for instanceas described above in reference to FIGS. 1-6. Receiving the alimentarytransfer request may include receiving, from a constitutional guidancesystem 116 an alimentary instruction set and generating the alimentarytransfer request as a function of the alimentary instruction set, forexample as described above in reference to FIGS. 1-6. Process selectiondevice 104 may further receive a user selection of at least analimentary instruction of the alimentary instruction set and generatethe alimentary transfer request as a function of the user selection ofthe at least an alimentary instruction, for instance as described abovein reference to FIGS. 1-6.

At step 710, and still referring to FIG. 7, process selection device 104provides at least categorical constraint modifying the alimentarytransfer request; this may be implemented as described above inreference to FIGS. 1-6. Providing at least a categorical constraint mayinclude receiving the at least a categorical constraint from a userdevice 108, for instance as described above in reference to FIGS. 1-6.Providing at least a categorical constraint may include receiving, froma constitutional guidance system 116 an alimentary instruction set andgenerating the at least a categorical constraint as a function of thealimentary instruction set, for instance as described above in referenceto FIGS. 1-6.

At step 715, and still referring to FIG. 7, process selection devicegenerates a plurality of alimentary transfer descriptors, wherein eachalimentary transfer descriptor describes a physical transfer process, ofa plurality of physical transfer processes, to be performed by acorresponding physical performance entity of a plurality of physicalperformance entities, each alimentary transfer descriptor describes analimentary collation to be provided during a corresponding physicaltransfer process, and each alimentary transfer descriptor furtherincludes a plurality of attributes, each attribute corresponding to aselection criterion of a plurality of selection criteria; this may beimplemented as described above in reference to FIGS. 1-6. Generating theplurality of alimentary transfer descriptors further comprisesreceiving, from an origin point device 112, an alimentary collationdescriptor, and matching, by the process selection device 104, thealimentary collation descriptor to the alimentary transfer request, forinstance as described above in reference to FIGS. 1-6.

At step 720, process selection device 104 selects, an alimentarytransfer descriptor of the plurality of alimentary transfer descriptors,for instance as described above in reference to FIGS. 1-6. Selecting mayinclude executing a selection function on the plurality of alimentarytransfer descriptors, wherein the selection function generates aselection output as a function of the plurality of selection criteria,the plurality of attributes, and the categorical constraint, forinstance as described above in reference to FIGS. 1-6. Executing theselection function may include executing a loss function of theplurality of attributes, for instance as described above in reference toFIGS. 1-6. Process selection device 104 may further provide trainingdata based on at least a past user interaction and generate the lossfunction using a machine learning algorithm as a function of thetraining data, for instance as described above in reference to FIGS.1-6. Selection of alimentary transfer descriptor further includesselecting the alimentary transfer descriptor based on the selectionoutput

At step 725, process selection device 104 transmits selected alimentarytransfer descriptor to the physical performance entity corresponding tothe selected alimentary transfer descriptor, for instance as describedabove in reference to FIGS. 1-6.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods andsystems according to the instant disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A method of selecting an alimentary transferdescriptor using categorical constraints, the method comprising:receiving, by a process selection device, an alimentary transferrequest, said alimentary transfer request including at least adescription of an alimentary collation and at least a terminal location;receiving, by the process selection device, an alimentary instructionset; providing, by the process selection device, at least a categoricalconstraint; modifying, by the process selection device, the alimentarytransfer request as a function of the alimentary instruction set and theat least a categorical restraint, said modified alimentary transferrequest identifying a modified alimentary collation; generating, by theprocess selection device and as a function of the modified alimentarytransfer request, a plurality of alimentary transfer descriptors,wherein: each alimentary transfer descriptor describes a respectivephysical transfer process of a plurality of physical transfer processes,to be performed by a corresponding physical performance entity; eachalimentary transfer descriptor describes the modified alimentarycollation to be provided during a corresponding physical transferprocess; and each alimentary transfer descriptor further includes aplurality of attributes, each attribute corresponding to a respectiveselection criterion of a plurality of selection criteria; generating aloss function of a plurality of attributes, wherein generating the lossfunction comprises: training a machine-learning model as a function ofat least a past user interaction: and outputting the loss function as afunction of the training data and the machine learning model (·);selecting an alimentary transfer descriptor from the plurality ofalimentary transfer descriptors as a function the plurality ofattributes and the categorical constraint by executing the loss functionof the plurality of attributes; and transmitting, by the processselection device, the selected alimentary transfer descriptor to thephysical performance entity corresponding to the selected alimentarytransfer descriptor.
 2. The method of claim 1, wherein the alimentarytransfer request is received from a user client device.
 3. The method ofclaim 1 further comprising: receiving a user selection of at least analimentary instruction of the alimentary instruction set; and generatingthe alimentary transfer request as a function of the user selection ofthe at least an alimentary instruction.
 4. The method of claim 1,wherein providing the at least a categorical constraint furthercomprises receiving the at least a categorical constraint from a userclient device.
 5. The method of claim 1, wherein providing the at leasta categorical constraint further comprises: generating the at least acategorical constraint as a function of the alimentary instruction set.6. The method of claim 1, wherein generating the plurality of alimentarytransfer descriptors further comprises: receiving, from an origin pointdevice, an alimentary collation descriptor; and matching, by the processselection device, the alimentary collation descriptor to the alimentarytransfer request.
 7. The method of claim 1, wherein modifying thealimentary transfer request comprises comparing an alimentary collationidentified by the alimentary instruction set to the alimentary collationdescribed in the alimentary transfer request.
 8. The method of claim 6,wherein receiving an alimentary collation descriptor comprises receivingan alimentary collation descriptor previously received.
 9. The method ofclaim 1, wherein transmitting alimentary transfer descriptor to aphysical performance entity further comprises transmitting dataindicating selection of a physical transfer process previouslytransmitted.
 10. A system for selecting an alimentary transferdescriptor using categorical constraints, the system comprising: aprocess selection device, the process selection device designed andconfigured to: receive an alimentary transfer request, said alimentarytransfer request including at least a description of an alimentarycollation and at least a terminal location receive an alimentaryinstruction set; provide at least a categorical constraint; modify thealimentary transfer request as a function of the alimentary instructionset and the at least a categorical restraint, said modified alimentarytransfer request identifying a modified alimentary collation; andtransmit a selected alimentary transfer descriptor to a physicalperformance entity corresponding to the selected alimentary transferdescriptor; a descriptor generator module operating on the processselection device, the descriptor generator module designed andconfigured to: generate a plurality of alimentary transfer descriptorsas a function of the modified alimentary transfer request, wherein; eachalimentary transfer descriptor describes a respective physical transferprocess of a plurality of physical transfer processes, to be performedby a corresponding physical performance entity; each alimentary transferdescriptor describes the modified alimentary collation to be providedduring a corresponding physical transfer process; and each alimentarytransfer descriptor further includes a plurality of attributes, eachattribute corresponding to a respective selection criterion of aplurality of selection criteria; a selector module operating on theprocess selection device, the selector module designed and configuredto: generate a loss function of a plurality of attributes, whereingenerating the loss function comprises: train a machine-learning modelas a function of at least a past user interaction; and output the lossfunction as a function of the training data and the machine learningmodel (·); select, by a machine learning model, an alimentary transferdescriptor from the plurality of alimentary transfer descriptors as afunction of the plurality of attributes and the categorical constraintby executing the loss function of the plurality of attributes.
 11. Thesystem of claim 10, wherein the process selection device is furtherconfigured to receive the alimentary transfer request from a userdevice.
 12. The system of claim 10 wherein the process selection deviceis further configured to: receive a user selection of at least analimentary instruction of the alimentary instruction set; and generatethe alimentary transfer request as a function of the user selection ofthe at least an alimentary instruction.
 13. The system of claim 10,wherein the process selection device is further configured to providethe at least a categorical constraint by receiving the at least acategorical constraint from a user client device.
 14. The system ofclaim 10, wherein the process selection device is further configured toprovide the at least a categorical constraint by: generating the atleast a categorical constraint as a function of the alimentaryinstruction set.
 15. The system of claim 10, wherein the descriptorgenerator module designed and configured to: generate a plurality ofalimentary transfer descriptors by: receiving, from an origin pointdevice, an alimentary collation descriptor; and matching the alimentarycollation descriptor to the alimentary transfer request.
 16. The methodof claim 7, wherein the modified alimentary collation comprises one ormore alimentary collations identified by the alimentary instruction set.17. The system of claim 10, wherein modifying the alimentary transferrequest comprises comparing an alimentary collation identified by thealimentary instruction set to the alimentary collation described in thealimentary transfer request.
 18. The system of claim 17, wherein themodified alimentary collation comprises one or more alimentarycollations identified by the alimentary instruction set.
 19. The systemof claim 15, wherein the descriptor generator module is designed andconfigured to receive an alimentary collation descriptor previouslyreceived.
 20. The system of claim 10, wherein the process selectiondevice is further designed and configured to transmit a selectedalimentary transfer descriptor by transmitting data indicating selectionof a physical transfer process previously transmitted.